Publications de Volkan Cevher | EPFL Graph Search

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We analytically investigate how over-parameterization of models in randomized machine learning algorithms impacts the information leakage about their training data. Specifically, we prove a privacy bound for the KL divergence between model distributions on ...

Compressive sensing (CS) is a data acquisition and recovery technique for finding sparse solutions to linear inverse problems from sub-Nyquist measurements. CS features a wide range of computationally efficient and robust signal recovery methods, based on ...

In this paper, we propose a novel splitting method for finding a zero point of the sum of two monotone operators where one of them is Lipschizian. The weak convergence the method is proved in real Hilbert spaces. Applying the proposed method to composite m ...

We consider the problem of learning multi-ridge functions of the form f (x) = g(Ax) from point evaluations of f. We assume that the function f is defined on an l(2)-ball in R-d, g is twice continuously differentiable almost everywhere, and A is an element ...

Defending aligned Large Language Models (LLMs) against jailbreaking attacks is a challenging problem, with existing approaches requiring multiple requests or even queries to auxiliary LLMs, making them computationally heavy. Instead, we focus on detecting ...

Choosing a distance preserving measure or metric is fun- damental to many signal processing algorithms, such as k- means, nearest neighbor searches, hashing, and compressive sensing. In virtually all these applications, the efficiency of the signal process ...

This paper proposes a tradeoff between sample complexity and computation time that applies to statistical estimators based on convex optimization. As the amount of data increases, we can smooth optimization problems more and more aggressively to achieve ac ...

We consider minimization problems that are compositions of convex functions of a vector

\x \in\reals^N

with submodular set functions of its support (i.e., indices of the non-zero coefficients of

\x

). Such problems are in general difficult for large

N

...

We introduce a sampling perspective to tackle the challenging task of training robust Reinforcement Learning (RL) agents. Leveraging the powerful Stochastic Gradient Langevin Dynamics, we present a novel, scalable two-player RL algorithm, which is a sampli ...

In this paper, we consider the problem of Gaussian process (GP) optimization with an added robustness requirement: The returned point may be perturbed by an adversary, and we require the function value to remain as high as possible even after this perturba ...

Training data mixtures greatly impact the generalization performance of large language models. Existing domain reweighting methods often rely on costly weight computations and require retraining when new data is introduced. To this end, we introduce a flex ...

Sparse recovery from undersampled random quan- tization measurements is a recent active research topic. Previous work asserts that stable recovery can be guaranteed via the basis pursuit dequantizer (BPDQ) if the measurements number is large enough, consid ...

We develop a flexible stochastic approximation framework for analyzing the long-run behavior of learning in games (both continuous and finite). The proposed analysis template incorporates a wide array of popular learning algorithms, including gradient-base ...

Universal methods for optimization are designed to achieve theoretically optimal convergence rates without any prior knowledge of the problem’s regularity parameters or the accurarcy of the gradient oracle employed by the optimizer. In this regard, existin ...

Group-based sparsity models are proven instrumental in linear regression problems for recovering signals from much fewer measurements than standard compressive sensing. The main promise of these models is the recovery of ``interpretable" signals along with ...

This paper addresses intra-client and inter-client covariate shifts in federated learning (FL) with a focus on the overall generalization performance. To handle covariate shifts, we formulate a new global model training paradigm and propose Federated Impor ...

The convex l(1)-regularized log det divergence criterion has been shown to produce theoretically consistent graph learning. However, this objective function is challenging since the l(1)-regularization is nonsmooth, the log det objective is not globally Li ...

This paper demonstrates how to recover causal graphs from the score of the data distribution in non-linear additive (Gaussian) noise models. Using score matching algorithms as a building block, we show how to design a new generation of scalable causal disc ...

This work presents an area and power efficient encoding system for wireless implantable devices capable of monitoring the electrical activity of the brain. Such devices are becoming an important tool for understanding, real-time monitoring, and potentially ...

We propose a new adaptive optimization algorithm based on mirror descent for a class of possibly non-convex smooth bilevel optimization problems. The bilevel optimization template is broadly applicable in machine learning as it features two coupled problem ...

Volkan Cevher

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Initialization Matters: Privacy-Utility Analysis of Overparameterized Neural Networks

Recipes on Hard Thresholding Methods

A reflected forward-backward splitting method for monotone inclusions involving Lipschitzian operators

Learning non-parametric basis independent models from point queries via low-rank methods

Single-pass Detection of Jailbreaking Input in Large Language Models

Metric Learning with Rank and Sparsity Constraints

Time–Data Tradeoffs by Aggressive Smoothing

To Convexify or Not? Regression with Clustering Penalties on Graphs

Robust Reinforcement Learning via Adversarial training with Langevin Dynamics

Adversarially Robust Optimization with Gaussian Processes

CHAMELEON: A Flexible Data-mixing Framework for Language Model Pretraining and Finetuning

A Learning-Based Framework for Quantized Compressed Sensing

A unified stochastic approximation framework for learning in games

UNDERGRAD: A Universal Black-Box Optimization Method with Almost Dimension-Free Convergence Rate Guarantees

Group-Sparse Model Selection: Hardness and Relaxations

Federated Learning under Covariate Shifts with Generalization Guarantees

Fast Proximal Algorithms For Self-Concordant Function Minimization With Application To Sparse Graph Selection

Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models

DCT Learning-Based Hardware Design for Neural Signal Acquisition Systems

Adaptive Bilevel Optimization